Study of the Ability of Knowledge Management and the Construction of Wisdom Capital
Bibliographic record
Abstract
This paper studied the concept of knowledge management and those factors, which influence it, and the constitution of KM, with the index of ability of KM. Also it studied the wisdom capital. Then the relation of KM and wisdom capital was researched following which the conclusion was gained that improve the ability of KM could be improved by the improvement of the ability of wisdom capital management. Key words: knowledge management (KM), intellectual capital, ability of KM Resume: Ce document etudie a la fois la comprehension de la gestion du savoir et les facteurs qui influencent la-dessus, les constituants de la capacite de la gestion du savoir et les indices mesurables de cette capacite, aussi le role du capital intellectuel base sur le travail intellectuel et l’evaluation de ce capital intellectuel. Apres l’analyse de la relation entre le capital intellectuel et la capacite de la gestion du savoir, la methode consiste a integrer la gestion du capital intellectuel dans celle du savoir, c’est-a-dire que l’amelioration de la capacite de la gestion du capital intellectuel mene necessairement a l’amelioration de la capacite de la gestion du savoir. Mot-cle: la gestion du savoir, le capital intellectuel, la capacite de la gestion du savoir
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".